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Cross-modality based celebrity face naming for news image collections

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Abstract

For automatically mining the underlying relationships between different famous persons in daily news, for example, building a news person based network with the faces as icons to facilitate face-based person finding, we need a tool to automatically label faces in new images with their real names. This paper studies the problem of linking names with faces from large-scale news images with captions. In our previous work, we proposed a method called Person-based Subset Clustering which is mainly based on face clustering for all face images derived from the same name. The location where a name appears in a caption, as well as the visual structural information within a news image provided informative cues such as who are really in the associated image. By combining the domain knowledge from the captions and the corresponding image we propose a novel cross-modality approach to further improve the performance of linking names with faces. The experiments are performed on the data sets including approximately half a million news images from Yahoo! news, and the results show that the proposed method achieves significant improvement over the clustering-only methods.

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Correspondence to Xueping Su.

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This work is supported by the doctorate foundation of Northwestern Polytechnical University under CX201114, Ministry of Education Fund for Doctoral Students Newcomer Awards of China, National Natural Science Foundation of China under Grant 61075014, 61272285, 61103062, The Research Fund for the Doctoral Program of Higher Education under Grant 20106102110028, 20116102110027, 20116102120031, 20126101110022, The Science and technology project of Shaanxi Province under Grant 2013K06-29, and NPU Basic Research Foundation under Grant JC201249.

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Su, X., Peng, J., Feng, X. et al. Cross-modality based celebrity face naming for news image collections. Multimed Tools Appl 73, 1643–1661 (2014). https://doi.org/10.1007/s11042-013-1578-6

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  • DOI: https://doi.org/10.1007/s11042-013-1578-6

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